Real World Evidence Associate Director Barcelona, Spain
Job Description AstraZeneca is a global, science-led, patient-focused biopharmaceutical company that focuses on the discovery, development and commercialization of prescription medicines for some of the world's most serious diseases. But we're more than one of the world's leading pharmaceutical companies.
Oncology is driven by speed. Here you will be backed by leadership and empowered at every level to prioritize and make ambitious moves. Be a daring decision-maker. Speak up and constructively challenge. Powered to take sensible risks based on scientific evidence. Here it's our scale, agility and passion that makes sure we deliver fast, every time.
The Oncology Real World Evidence R&D team is a new group growing within AstraZeneca. AstraZeneca has a pedigree of experience in Real World Evidence, having developed a coherent strategy to develop and internalize rich data assets the group is now amplifying those investments through a Real World Evidence Data Science capability.
We are looking for a quantitative epidemiologists, bioinformaticians, health informaticians, biomedical data scientists, clinicians/pharmacologists, bio-statisticians or related fields with a strong desire to learn and expand their abilities into the analysis of Real World Evidence (RWE).
The role holder will be a subject matter expert on the use of Real World Data and its capabilities in the use of RWD. The role holder will transform real-world clinical data into concrete insights for clinical development using statistical methods and innovative data visualizations to support decisions. The individual will also be responsible to the ideation of new methods and applications of RWE for new clinical development challenges and will be responsible for supporting new regulatory interactions using RWD.
The AstraZeneca Oncology R&D RWE group provide expert analysis and interpretation of the complex biomedical data captured in electronic health records, claims data, registries, wearables and epidemiological observations. This important work, which provides a rich window on the complicated realities of patients and diseases, is used to support the drug development process in a variety of ways, including:
Supporting clinical project teams understand the benefits of RWD and support them in their clinical design
Developing close connections with biometrics and clinical teams to develop a strategy for RWD use within a drug development program
Interact with senior stakeholders to ensure the value of RWD is understood and supported within a Research and Development setting
Analyzing longitudinal health data to characterize patient journeys and outcomes
Sifting claims and prescription data for use patterns and to support label expansion
Building predictive models of patient outcomes
Identifying patient subtypes (e.g. via biomarkers) for possible therapy development
Building synthetic control arms to support the interpretation of clinical studies
Development of algorithms for better diagnosis and identification of patients
Searching for evidence of adverse effects in medical histories
Estimating the number of eligible patients for clinical trials from databases and literature
Using federated networks of electronic health records for patient identification and recruitment
Using real world evidence to support pragmatic and hybrid trial designs
Partnering with external organizations to generate custom real world datasets
Minimum Qualifications: Master's degree + 5+ years of relevant experience
Experience in supporting a multidisciplinary team build a research objective that can be met with RWD
Experience in the use and application of RWD to support clinical decision making
Health analytics and data mining of routinely collected healthcare data
Use of statistical and scripting languages such as R, Python and SQL
Clinical trials and recruitment, especially the application of synthetic control arms
The application of genomics in clinical care or translational medicine
Health economics or epidemiology, and quantitative science such as health outcome modelling
Desirable Skills PhD Degree
Data science, machine learning and construction of predictive models
Clinical data standards, medical terminologies and healthcare ontologies
Work in a patient care or similar setting, that would allow the candidate to bring medical perspective into real-world evidence generation
Experience designing and implementing pragmatic clinical trials
Knowledge of Oncology and Pharmaceutical development
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